README.md

metadata

pipeline_tag: image-classification

Model Card: Fine-Tuned InceptionV3 & Xception for Human Decomposition Image Classification

These CNN models were developed for the classification of human decomposition images into various stage of decay categories, including fresh, early decay, advanced decay, and skeletonized (Megyesi et al., 2005).

Model Details

Model Description

• Developed by: Anna-Maria Nau
• Funded by: National Institute of Justice
• Model type: CNNs for Image Classification
• Base Model: InceptionV3 and Xception pretrained on ImageNet
• Transfer Learning Method: Two-step transfer learning: (1) freeze all pre-trained convolutional layers of the base model and train newly added classifier layers on custom dataset and (2) unfreeze all layers, and fine-tune model end-to-end on custom dataset.

Model Sources

• Paper :
  • Stage of Decay Estimation Exploiting Exogenous and Endogenous Image Attributes to Minimize Manual Labeling Efforts and Maximize Classification Performance
  • Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach

Usage

The stage of decay classification is bodypart specific, that is, for the head, torso, or limbs.